Integrating multiplex immunofluorescent and mass spectrometry imaging to map myeloid heterogeneity in its metabolic and cellular context

Integration of mass cytometry and mass spectrometry imaging for spatially resolved single-cell metabolic profiling

The integration of spatial omics technologies can provide important insights into the biology of tissues. Here we combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on a single tissue section to reveal metabolic heterogeneity at single-cell resolution within tissues and its association with specific cell populations such as cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components.

Phenotypic, Metabolic, and Functional Characterization of Experimental Models of Foamy Macrophages: Toward Therapeutic Research in Atherosclerosis

Different types of macrophages (Mφ) are involved in atherogenesis, including inflammatory Mφ and foamy Mφ (FM). Our previous study demonstrated that two-photon excited fluorescence (TPEF) imaging of NADH and FAD autofluorescence (AF) could distinguish experimental models that mimic the different atherosclerotic Mφ types. The present study assessed whether optical differences correlated with phenotypic and functional differences, potentially guiding diagnostic and therapeutic strategies. Phenotypic differences were investigated using three-dimensional principal component analysis and multi-color flow cytometry. Functional analyses focused on cytokine production, metabolic profiles, and cellular oxidative stress, in LDL dose-dependent assays, to understand the origin of AF in the FAD spectrum and assess FM ability to transition toward an immunoregulatory phenotype and function. Phenotypic studies revealed that FM models generated with acetylated LDL (Mac) were closer to immunoregulatory Mφ, while those generated with oxidized LDL (Mox) more closely resembled inflammatory Mφ. The metabolic analysis confirmed that inflammatory Mφ primarily used glycolysis, while immunoregulatory Mφ mainly depended on mitochondrial respiration. FM models employed both pathways; however, FM models generated with high doses of modified LDL showed reduced mitochondrial respiration, particularly Mox FM. Thus, the high AF in the FAD spectrum in Mox was not linked to increased mitochondrial respiration, but correlated with the dose of oxidized LDL, leading to increased production of reactive oxygen species (ROS) and lysosomal ceroid accumulation. High FAD-like AF, ROS, and ceroid accumulation were reduced by incubation with α-tocopherol. The cytokine profiles supported the phenotypic analysis, indicating that Mox FM exhibited greater inflammatory activity than Mac FM, although both could be redirected toward immunoregulatory functions, albeit to different degrees. In conclusion, in the context of immunoregulatory therapies for atherosclerosis, it is crucial to consider FM, given their prevalence in plaques and our results, as potential targets, regardless of their inflammatory status, alongside non-foamy inflammatory Mφ.

Spatio-temporal plant hormonomics: from tissue to subcellular resolution

Due to technological advances in mass spectrometry, significant progress has been achieved recently in plant hormone research. Nowadays, plant hormonomics is well established as a fully integrated scientific field focused on the analysis of phytohormones, mainly on their isolation, identification, and spatiotemporal quantification in plants. This review represents a comprehensive meta-study of the advances in the phytohormone analysis by mass spectrometry over the past decade. To address current trends and future perspectives, Web of Science data were systematically collected and key features such as mass spectrometry-based analyses were evaluated using multivariate data analysis methods. Our findings showed that plant hormonomics is currently divided into targeted and untargeted approaches. Both aim to miniaturize the sample, allowing high-resolution quantification to be covered in plant organs as well as subcellular compartments. Therefore, we can study plant hormone biosynthesis, metabolism, and signalling at a spatio-temporal resolution. Moreover, this trend has recently been accelerated by technological advances such as fluorescence-activated cell sorting or mass spectrometry imaging.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Multiplex Imaging for Cell Phenotyping of Early Human Atherosclerosis

BACKGROUND

Previous studies using animal models and cultured cells suggest that vascular smooth muscle cells (SMCs) and inflammatory cytokines are important players in atherogenesis. Validating these findings in human disease is critical to designing therapeutics that target these components. Multiplex imaging is a powerful tool for characterizing cell phenotypes and microenvironments using biobanked human tissue sections. However, this technology has not been applied to human atherosclerotic lesions and needs to first be customized and validated.

METHODS AND RESULTS

For validation, we created an 8-plex imaging panel to distinguish foam cells from SMC and leukocyte origins on tissue sections of early human atherosclerotic lesions (n=9). The spatial distribution and characteristics of these foam cells were further analyzed to test the association between SMC phenotypes and inflammation. Consistent with previous reports using human lesions, multiplex imaging showed that foam cells of SMC origin outnumbered those of leukocyte origin and were enriched in the deep intima, where the lipids accumulate in early atherogenesis. This new technology also found that apoptosis or the expression of pro-inflammatory cytokines were not more associated with foam cells than with nonfoam cells in early human lesions. More CD68+ SMCs were present among SMCs that highly expressed interleukin-1β. Highly inflamed SMCs showed a trend of increased apoptosis, whereas leukocytes expressing similar levels of cytokines were enriched in regions of extracellular matrix remodeling.

CONCLUSIONS

The multiplex imaging method can be applied to biobanked human tissue sections to enable proof-of-concept studies and validate theories based on animal models and cultured cells.

Spatial metabolomics and its application in the liver

Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

Spatial pharmacology using mass spectrometry imaging

The emerging and powerful field of spatial pharmacology can map the spatial distribution of drugs and their metabolites, as well as their effects on endogenous biomolecules including metabolites, lipids, proteins, peptides, and glycans, without the need for labeling. This is enabled by mass spectrometry imaging (MSI) that provides previously inaccessible information in diverse phases of drug discovery and development. We provide a perspective on how MSI technologies and computational tools can be implemented to reveal quantitative spatial drug pharmacokinetics and toxicology, tissue subtyping, and associated biomarkers. We also highlight the emerging potential of comprehensive spatial pharmacology through integration of multimodal MSI data with other spatial technologies. Finally, we describe how to overcome challenges including improving reproducibility and compound annotation to generate robust conclusions that will improve drug discovery and development processes.

Macrophage heterogeneity in atherosclerosis: A matter of context

During atherogenesis, plaque macrophages take up and process deposited lipids, trigger inflammation, and form necrotic cores. The traditional inflammatory/anti-inflammatory paradigm has proven insufficient in explaining their complex disease-driving mechanisms. Instead, we now appreciate that macrophages exhibit remarkable heterogeneity and functional specialization in various pathological contexts, including atherosclerosis. Technical advances for studying individual cells, especially single-cell RNA sequencing, indeed allowed to identify novel macrophage subsets in both murine and human atherosclerosis, highlighting the existence of diverse macrophage activation states throughout pathogenesis. In addition, recent studies highlighted the role of the local microenvironment in shaping the macrophages' phenotype and function. However, this remains largely undescribed in the context of atherosclerosis. In this review we explore the origins of macrophages and their functional specialization, shedding light on the diverse sources of macrophage accumulation in the atherosclerotic plaque. Next, we discuss the phenotypic diversity observed in both murine and human atherosclerosis, elucidating their distinct functions and spatial distribution within plaques. Finally, we highlight the importance of the local microenvironment in both phenotypic and functional specialization of macrophages in atherosclerosis and elaborate on the need for spatial multiomics approaches to provide a better understanding of the different macrophage subsets' roles in the pathogenesis of atherosclerosis.

Protocol for multispectral imaging on cryosections to map myeloid cell heterogeneity in its spatial context

Recent technical advances, such as single-cell RNA sequencing and mass cytometry, improve identification of cell types and subsets in a range of healthy and diseased tissues at the expense of their cellular and molecular context. Here, we present a protocol for in situ multispectral imaging to map myeloid cell heterogeneity in tissue cryosections, describing steps for cutting sequential sections, antibody titration, and building a spectral library. We then detail procedures for multispectral imaging and preparing data for downstream analysis. For complete details on the use and execution of this protocol, please refer to Goossens et al. (2022).1.

State-of-the-art mass spectrometry imaging applications in biomedical research

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.

Tissue-specific macrophage immunometabolism

Macrophages are phagocytic cells distributed across tissues that sustain homeostasis by constantly probing their local environment. Upon perturbations, macrophages rewire their energy metabolism to execute their immune programs. Intensive research in the field of immunometabolism highlights cell-intrinsic immunometabolites such as succinate and itaconate as immunomodulatory signals. A role for cell-extrinsic stimuli now emerges with evidence for signals that shape macrophages' metabolism in a tissue-specific manner. In this review, we will cover macrophage immunometabolism in the gut, a complex metabolic and immunologically active tissue. During homeostasis, gut macrophages are constantly exposed to pro-inflammatory ligands from the microbiota, and in contrast, are balanced by microbiota-derived anti-inflammatory metabolites. Given their extensive metabolic changes during activation, spatial analyses of the tissue will allow the characterization of metabolic niches of macrophage in the gut. Identifying metabolic perturbations of macrophage subsets during chronic inflammation and infection can direct future tissue-specific metabolotherapies.

Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments

Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and the underlying biology and clinical features, but there is significant lag in the development of downstream analysis methods and benchmarking tools. Here we present SPIAT (spatial image analysis of tissues), a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial simulator), a simulator of tissue spatial data. SPIAT includes multiple colocalization, neighborhood and spatial heterogeneity metrics to characterize the spatial patterns of cells. Ten spatial metrics of SPIAT are benchmarked using simulated data generated with spaSim. We show how SPIAT can uncover cancer immune subtypes correlated with prognosis in cancer and characterize cell dysfunction in diabetes. Our results suggest SPIAT and spaSim as useful tools for quantifying spatial patterns, identifying and validating correlates of clinical outcomes and supporting method development.

Systems Immunology Approaches to Metabolism

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.

The heterogeneous cellular landscape of atherosclerosis: Implications for future research and therapies. A collaborative review from the EAS young fellows

Single cell technologies, lineage tracing mouse models and advanced imaging techniques unequivocally improved the resolution of the cellular landscape of atherosclerosis. Although the discovery of the heterogeneous nature of the cellular plaque architecture has undoubtedly improved our understanding of the specific cellular states in atherosclerosis progression, it also adds more complexity to current and future research and will change how we approach future drug development. In this review, we will discuss how the revolution of new single cell technologies allowed us to map the cellular networks in the plaque, but we will also address current (technological) limitations that confine us to identify the cellular drivers of the disease and to pinpoint a specific cell state, cell subset or cell surface antigen as new candidate drug target for atherosclerosis.

Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution

We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method's potential through its application toward delineating chemical traits of Alzheimer's disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Heterogeneity of macrophages in atherosclerosis revealed by single-cell RNA sequencing

Technology at the single-cell level has advanced dramatically in characterizing molecular heterogeneity. These technologies have enabled cell subtype diversity to be seen in all tissues, including atherosclerotic plaques. Critical in atherosclerosis pathogenesis and progression are macrophages. Previous studies have only determined macrophage phenotypes within the plaque, mainly by bulk analysis. However, recent progress in single-cell technologies now enables the comprehensive mapping of macrophage subsets and phenotypes present in plaques. In this review, we have updated and discussed the definition and classification of macrophage subsets in mice and humans using single-cell RNA sequencing. We summarized the different classification methods and perspectives: traditional classification with an updated scoring system, inflammatory macrophages, foamy macrophages, and atherosclerotic-resident macrophages. In addition, some special types of macrophages were identified by specific markers, including IFN-inducible and cavity macrophages. Furthermore, we discussed macrophage subset-specific markers and their functions. In the future, these novel insights into the characteristics and phenotypes of these macrophage subsets within atherosclerotic plaques can provide additional therapeutic targets for cardiovascular diseases.